Added logger

rag_system/loaders/pdf_loader.py

@@ -1,11 +1,15 @@
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import PyPDFLoader
 
+import logging
+
+logger: logging.Logger = logging.getLogger("pdf_loader")
+
 
 def load_pdf(file_path):
     loader = PyPDFLoader(file_path)
     splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
     documents = loader.load_and_split(splitter)
-    print(f"Loaded and Split into {len(documents)} documents from {file_path}")
+    logger.info(f"Loaded and Split into {len(documents)} documents from {file_path}")
 
     return documents

rag_system/loaders/web_loader.py

@@ -2,6 +2,8 @@ import json
 import os
 import logging
 
+logger: logging.Logger = logging.getLogger("web_loader")
+
 from langchain_community.document_loaders import WebBaseLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from rag_system.loaders.firecrawl import FireCrawlLoader
@@ -11,9 +13,6 @@ from dotenv import load_dotenv
 
 load_dotenv()  # take environment variables]
 
-# Configure the logging
-logging.basicConfig(level=logging.INFO)
-
 firecrawl_api_key = os.getenv("FIRECRAWL_API_KEY")
 firecrawl_api_url = os.getenv("FIRECRAWL_API_URL")
 firecrawl_mode = os.getenv("FIRECRAWL_MODE")
@@ -27,14 +26,14 @@ if firecrawl_params["scrape_options"]:
     )
 
 
-logging.info(f"web_loader firecrawl_params: {firecrawl_params}")
+logger.info(f"web_loader firecrawl_params: {firecrawl_params}")
-logging.info(f"web_loader firecrawl_api_url: {firecrawl_api_url}")
+logger.info(f"web_loader firecrawl_api_url: {firecrawl_api_url}")
-logging.info(f"web_loader firecrawl_mode: {firecrawl_mode}")
+logger.info(f"web_loader firecrawl_mode: {firecrawl_mode}")
-logging.info(f"web_loader firecrawl_params: {firecrawl_params}")
+logger.info(f"web_loader firecrawl_params: {firecrawl_params}")
 
 
 def load_web_crawl(url):
-    logging.info(f"load_web_crawl url: {url}")
+    logger.info(f"load_web_crawl url: {url}")
 
     loader = FireCrawlLoader(
         url=url,
@@ -46,9 +45,7 @@ def load_web_crawl(url):
     docs = []
     docs_lazy = loader.load()
     for doc in docs_lazy:
-        print(".", end="")
         docs.append(doc)
-    print()
 
     # Load documents from the URLs
     # docs = [WebBaseLoader(url).load() for url in urls]

rag_system/vectordb/azure_search.py

@@ -1,4 +1,7 @@
 import os
+import logging
+
+logger: logging.Logger = logging.getLogger("azure_search")
 
 from typing import Tuple
 from langchain_community.vectorstores.azuresearch import (
@@ -106,24 +109,24 @@ def get_document_id(document):
         raise ValueError("Document does not have a valid ID.")
 
 
-def delete_all_documents():
+def delete_documents(filters: str = None):
     """
-    Delete all documents from the AzureSearch vector store.
+    Delete documents from the AzureSearch vector store.
     """
     try:
 
         docs_to_delete = []
         while True:
             # Delete all documents in the index
-            docs_to_delete = retrieve("", 10)
+            docs_to_delete = vector_store.vector_search("*", 20, filters=filters)
-
+            logger.debug("Deleting documents: %s", docs_to_delete)
             vector_store.delete(list(map(get_document_id, docs_to_delete)))
             if len(docs_to_delete) > 0:
                 continue
             else:
                 break
 
-        print("All documents deleted successfully.")
+        logger.debug("All documents deleted successfully.")
     except Exception as e:
         print(f"Error deleting documents: {str(e)}")
 
@@ -134,7 +137,7 @@ def add_documents(documents):
     try:
         vector_store.add_documents(documents)
     except Exception as e:
-        print(f"Error adding document to vector store: {str(e)}")
+        logger.error(f"Error adding document to vector store: {str(e)}")
 
 
 def retrieve(query_text, n_results=1):
@@ -145,46 +148,3 @@ def retrieve(query_text, n_results=1):
         search_type="similarity",
     )
     return docs
-
-
-# def add_document_to_vector_store(document):
-#     """
-#     Add a document to the AzureSearch vector store.
-
-#     Args:
-#         vector_store: The initialized AzureSearch vector store instance.
-#         document: A dictionary or object representing the document to be added.
-#                   Example format:
-#                   {
-#                       "id": "unique_document_id",
-#                       "content": "The text content of the document",
-#                       "metadata": {
-#                           "source": "source_url",
-#                           "created": "2025-03-04T14:14:40.421666",
-#                           "modified": "2025-03-04T14:14:40.421666"
-#                       }
-#                   }
-#     """
-#     try:
-
-#         # Add the document to the vector store
-#         vector_store.add_documents([document])
-#         print(f"Document with ID {document['id']} added successfully.")
-#     except Exception as e:
-#         print(f"Error adding document to vector store: {str(e)}")
-
-# add_document_to_vector_store("https://api.python.langchain.com/en/latest/langchain_api_reference.html",None)
-# Example document to add
-
-# doc = Document(
-#                 page_content="This is the content of the document.For testing IVA demo integration  ",
-#                 metadata= {
-#                     "source": "https://example.com/source",
-#                     "created": "2025-03-04T14:14:40.421666",
-#                     "modified": "2025-03-04T14:14:40.421666"
-#                 }
-#             )
-# Add the document to the vector store
-# add_document_to_vector_store( doc)
-
-# result = retrieve("iva",1)